Smooth Boosting for Margin-Based Ranking

We propose a new boosting algorithm for bipartite ranking problems. Our boosting algorithm, called SoftRankBoost, is a modification of RankBoost which maintains only smooth distributions over data. SoftRankBoost provably achieves approximately the maximum soft margin over all pairs of positive and negative examples, which implies high AUC score for future data.

• Type: Conference
• Year: 2008
• Where: ALT
• Authors: Jun-ichi Moribe, Kohei Hatano, Eiji Takimoto, Masayuki Takeda
• Keywords: ALT 2008, Machine Learning, Maximum Soft Margin, Smooth Distributions